Source: https://src.acm.org/grand-finalists/2021

Graduate Category – First Place

Jiaqi Gu, University of Texas at Austin
Research Advisors: David Z. Pan and Ray T. Chen

“Light in Artificial Intelligence: Efficient Neuromorphic Computing with Optical Neural Networks”
(ICCAD 2020)

Deep neural networks have received an explosion of interest for their superior performance in various intelligent tasks and high impacts on our lives. The computing capacity is in an arms race with the rapidly escalating model size and data amount for intelligent information processing. Practical application scenarios, e.g., autonomous vehicles, data centers, and edge devices, have strict energy efficiency, latency, and bandwidth constraints, raising a surging need to develop more efficient computing solutions. However, as Moore’s law is winding down, it becomes increasingly challenging for conventional electrical processors to support such massively parallel and energy-hungry artificial intelligence (AI) workloads. .. [Read more]

Undergraduate Category – Second Place

Chuangtao Chen, Zhejiang University
Research Advisor: Cheng Zhuo

“Optimally Approximated Floating-Point Multiplier”
(ICCAD 2020)

At the edge, IoT devices are designed to consume the minimum resource to achieve the desired accuracy. However, the conventional processors, such as CPU or GPU, can only conduct all the computations with predetermined but sometimes unnecessary precisions, inevitably degrading their energy efficiency. When running data-intensive applications, due to the large range of input operands, most conventional processors heavily rely on floating-point units (FPUs). Recently, approximate computing has become a promising alternative to improve energy efficiency for IoT devices on the edge, especially when running inaccuracy-tolerable applications. For various data-intensive tasks on edge devices, multiplication is a common but the most energy consuming one among different floating-point operations. As a common arithmetic component that has been studied for decades [1]–[3], the past focus on the FP multiplier is accuracy and performance… [Read more]